Embodiments of the present invention are related to the field of remote sensing or testing by polarized light examination, which comprises art from the field of polarimeters and art from the field of automated or aided discrimination, classification, and recognition of objects, materials, and material states through application of learning algorithms to polarized-light measurements. The object, material, or material state of interest is often called the target and this field of remote-sensing is often termed automated target recognition (ATR.)
Referring to FIG. 1, polarimeters can be categorized according to their radiation source as either a) active, which employ a laser or other controlled electromagnetic-radiation (EMR) source or b) passive, which employ solar or thermal electromagnetic radiation, or c) hybrid, which employ both active and passive sources. Polarimeters can be further subdivided according to the measurements they make, their polarization modulators, and their applications. Stokes polarimeters measure the Stokes parameters (or Stokes vector), which completely characterize the polarization properties of the EMR. If the source is unpolarized, as are most passive sources, then the Stokes parameters reveal all obtainable polarization properties of the illuminated object, hence Stokes polarimeters can be applied for passive remote sensing. Mueller-matrix polarimeters, as taught for instance in U.S. Pat. Nos. 4,306,809, 5,247,176, and 5,956,147, employ a controlled polarized source, often a laser, to measure the Mueller matrix, which characterizes the complete linear response of a material or object to an electromagnetic wave. U.S. Pat. No. 7,218,398 teaches application of the data obtained with a Mueller-matrix polarimeter to an inverse problem for material characterization. Ellipsometers are a subclass of Mueller-matrix polarimeters that solve the inverse problem under the assumption of no scattering or birefringence, which results in a sparse Mueller matrix, although generalized ellipsometry, as taught for instance in U.S. Pat. No. 7,218,398, relaxes certain of these assumptions. Most polarimeters designed for characterization are laboratory instruments that also comprise a sample stage to accurately control the position and orientation of the material or object sample. Polarimeters can also be categorized according to their polarization modulators. The majority of polarimeters rely on some combination of polarizing prisms or beamsplitters and rotating retarder waveplates, although alternatives including photoelastic modulators (PEM), liquid-crystal variable retarders (LCVR), and microgrid arrays have also been employed.
Polarimeters for remote sensing must be compact, lightweight, and typically fast, and their sensing schemes must work for materials and objects in generally arbitrary locations and orientations within the field-of-view. The most common approach is to limit the number of measurements or channels to 2, achievable in real-time using a polarizing prism or beamsplitter in the receiver, and to form the difference of the 2 channels. This class of polarimeter for remote sensing is taught for instance in U.S. Pat. No. 8,116,000 for passive sources and in U.S. Pat. No. 7,333,897 for active sources. Because most polarizing prisms and beamsplitters separate EMR into orthogonal polarization states (eg, vertical and horizontal linear states), most polarimeters of this type measure 2 orthogonal channels. For active 2-channel polarimeters the channels are often aligned parallel to and orthogonal to (or crossed with) the polarization direction of the illuminating EMR. Certain active polarimeters, for instance those taught in U.S. Pat. Nos. 5,956,147 and 6,060,710, employ photoelastic modulators (PEM), which require complex calibration and demodulation routines but can measure the Mueller matrix sufficiently fast for remote sensing.
In remote sensing the terms detect, discriminate, classify, recognize, and identify have formal meanings, although the terms are casually interchanged in many publications. As used herein the term “detect” means to record EMR reflected or transmitted by the target; the term “discriminate” means to distinguish the target from the background; the term “classify” means to distinguish the target or targets among various classes including the background and other non-targets, which are often referred to as clutter; the terms “recognize” and “identify” imply even more specificity. Distinguishing materials (eg, metal, plastic, or wood) is generally considered classification, while distinguishing an object as a known combination of materials and possibly shape may be considered recognition. The variability of measured data, and especially field data, requires the application of learning algorithms to anticipate the distributions of measured signatures. Learning algorithms also help exclude clutter and reduce false-alarms. Algorithms can be adapted from the field of machine-learning. Learning algorithms of varying sophistication have been applied to classify materials based on polarimeter measurements, as taught for instance in U.S. Pat. Nos. 6,060,710 and 7,333,897 and by Jones, et al., Proc. SPIE 6240, 62400A (2006) and by Hoover and Tyo, Applied Optics 46, 8364-8373 (2007).
Electromagnetic sensors that can rapidly and non-destructively discriminate, classify, and recognize materials, material states, and objects composed of those materials and states have many useful applications depending on the material sensitivities of the measured characteristics of the electromagnetic wave. Polarization at optical and near-optical frequencies is sensitive to broad ranges of materials and material states including metals, plastics, fibrous materials including fiber composites, crystals, chiral molecules, and biological tissues, and even broader ranges if patterned, stressed, or damaged materials are considered. Polarimeters have been demonstrated for the discrimination and classification of such materials and states, usually by increasing the contrast between a target material or material state and other materials or material states in the observation of reflected or transmitted EMR. Most polarimeters increase contrast by differencing irradiance channels defined by different settings of their polarization modulators. Ideally the irradiances of the target are different in the two channels, while the irradiances of non-targets are similar in the two channels, such that the difference channel provides increased target contrast. The polarimeter irradiance channels can be in the form of digital images or in the form of detector signals, multiplexed in time and/or space. For high-speed performance, polarimeters are usually limited to two simultaneous channels, as in the case of polarization-difference imaging (PDI), as taught for instance in U.S. Pat. Nos. 4,881,818, 5,929,443, and 8,116,000.
Active polarimeters comprise a source of controlled, polarized EMR, often a laser, that is typically formed into a beam that illuminates the sample or scene of interest. Compared to passive polarimeters, which utilize solar illumination or thermal emission, active polarimeters provide access to additional signature components and perform independently of weather and time-of-day. Active polarimeters designed for characterization, as taught for optical and infrared frequencies in U.S. Pat. Nos. 4,306,809, 5,247,176, 5,956,147, and 7,218,398, typically measure the complete polarization signature, also known as the Mueller matrix, for illumination at given angles and electromagnetic frequencies. These are typically large laboratory instruments with stages to control sample orientation and position and polarization modulators with high size, weight, and power (SWaP) requirements and data rates well below video rate. For instance, U.S. Pat. No. 7,218,398 specifies the maximum data rate of this type of polarimeter on the order of 1 Hz.
Mathematically, the Stokes parameters are written asS=[S0 S1 S2 S3]T,  (1)where S0 is the irradiance, S1 is the degree of linear polarization in the horizontal-vertical coordinate system, S2 is the degree of linear polarization at ±45° to the horizontal-vertical coordinates, S3 is the degree of circular polarization, and [ . . . ]T denotes a matrix transpose. The Stokes parameters define the polarization state of the EMR. The Mueller matrix is the linear transformation of the Stokes parameters upon interaction of the electromagnetic wave with a material object, written mathematically as
                              S          out                =                  MS          =                                                    [                                                                                                    M                        00                                                                                                            M                        01                                                                                                            M                        02                                                                                                            M                        03                                                                                                                                                M                        10                                                                                                            M                        11                                                                                                            M                        12                                                                                                            M                        13                                                                                                                                                M                        20                                                                                                            M                        21                                                                                                            M                        22                                                                                                            M                        23                                                                                                                                                M                        30                                                                                                            M                        31                                                                                                            M                        32                                                                                                            M                        33                                                                                            ]                            ⁡                              [                                                                                                    S                        0                                                                                                                                                S                        1                                                                                                                                                S                        2                                                                                                                                                S                        3                                                                                            ]                                      .                                              (        2        )            For mathematical analysis and design the Mueller matrix is often written in one-dimensional form as{tilde over (M)}=[M00 M01 M02 M03 M10 . . . M31 M32 M33].  (3)
The Stokes parameters are often referred to as the Stokes vector, which is correct only in the computing sense of a one-dimensional array of numbers. Since irradiance cannot be negative, both Stokes parameters and Mueller matrices lack additive inverses, so are not vectors in the algebraic sense.
Active polarimeters designed for remote sensing typically do not measure the complete Mueller matrix, but only parts of the signature that show contrast between the target of interest and the anticipated background and clutter, allowing SWaP to be reduced and speed increased due to fewer components and fewer measurements. For example, as taught in U.S. Pat. No. 7,580,127, most atmospheric aerosols and clouds exhibit a diagonal Mueller matrix with a single independent parameter that can be obtained from measurement of M11, which can be accomplished with an active polarimeter by forming the difference of two orthogonal channels. Nearly all active polarimeters designed for remote sensing are based on measurement of one or two polarization channels, with this limitation imposed by requirements for low SWaP and/or high speed. Vannier, et al., Applied Optics 55, 2881-2891 (2016) demonstrates the current public-domain state-of-the-art in polarimeters for optical remote sensing, employing LCVR modulators and an optimization routine to empirically derive and implement a single-channel active polarimeter that provides enhanced-contrast imaging of a specific class of materials. However, as shown in Hoover and Tyo, Applied Optics 46, 8364-8373 (2007), active polarization signatures of many relevant materials are actually multi-dimensional, and discrimination, classification, and recognition performance can be improved by applying learning algorithms to measured multi-dimensional signatures. In particular, multi-dimensional measurements are usually needed to achieve classification performance that is relatively invariant to the target orientation or pose, which is usually a critical requirement for remote sensing. Fielding an active polarimeter for high-speed, high-performance classification or ATR therefore requires a new class of polarimeter that can measure specific multi-dimensional signatures while maintaining the low SWaP and high speed achievable by 2-channel polarimeters.
Most active field sensors, including polarimeters, are configured for monostatic or near-monostatic geometries, due to the practical advantage of placing the source-transmitter and the receiver-detector on the same platform. Passive sensors, on the other hand, often operate at large bistatic angles, for instance as illustrated in FIG. 2. While the dimensionality of passive polarization signatures is generally too low to achieve pose invariance or match the classification performance of active polarimeters, for certain materials and objects the passive signature contains unique information at large bistatic angles that can augment the active signature and improve performance. There is therefore a need for field polarimeters to combine active and passive signature measurements in the same hybrid polarimeter in order to utilize more information and improve remote-sensing performance.